Anind K. Dey

Carnegie Mellon University | CMU · Human-Computer Interaction Institute

Objective. This study sought to examine how daily mind wandering is related to loneliness, felt connection to others, and school belonging among college students.Participants. Three samples (n = 209, n = 173, and n = 266) on two US campuses were recruited.Methods. Data were collected via ecological momentary assessment over the course of two academ...

BACKGROUND Acute marijuana intoxication can impair motor skills and cognitive functions (e.g., attention, information processing). However, existing tools (e.g., blood, urine, saliva tests) do not accurately reflect ‘real-time’ acute marijuana intoxication. OBJECTIVE Considering the absence of screening tools to detect acute marijuana intoxication...

Over the past few decades, ubiquitous sensors and systems have been an integral part of humans' everyday life. They augment human capabilities and provide personalized experiences across diverse contexts such as healthcare, education, and transportation. However, the widespread adoption of ubiquitous computing has also brought forth concerns regard...

The recent technology boost of large language models (LLMs) has empowered a variety of applications. However, there is very little research on understanding and improving LLMs' capability for the mental health domain. In this work, we present the first comprehensive evaluation of multiple LLMs, including Alpaca, Alpaca-LoRA, and GPT-3.5, on various...

Academic achievement in the first year of college is critical for setting students on a pathway toward long-term academic and life success, yet little is known about the factors that shape early college academic achievement. Given the important role sleep plays in learning and memory, here we extend this work to evaluate whether nightly sleep durat...

There is a growing body of research revealing that longitudinal passive sensing data from smartphones and wearable devices can capture daily behavior signals for human behavior modeling, such as depression detection. Most prior studies build and evaluate machine learning models using data collected from a single population. However, to ensure that...

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single popu...

BACKGROUND Digital behavioral interventions can reduce binge drinking events (BDEs: consuming 4+/5+ drinks per occasion for women/men) in young adults, however, they may not be optimized for timing or content. Delivering support in the hours prior to a predicted drinking event could improve the impact of that support. OBJECTIVE In this paper, our...

Background: Digital Just-In-Time Adaptive Interventions (JITAIs) can reduce binge drinking events (BDEs: consuming 4+/5+ drinks per occasion for women/men) in young adults, but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. Objective: We determin...

The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergradu...

BACKGROUND Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and preliminary effects of a real-time mobile intervention that detects and disrupts prolonged SB before and after canc...

Background Sedentary behavior (SB) is prevalent after abdominal cancer surgery, and interventions targeting perioperative SB could improve postoperative recovery and outcomes. We conducted a pilot study to evaluate the feasibility and preliminary effects of a real-time mobile intervention that detects and disrupts prolonged SB before and after canc...

This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evalua...

Feeling a sense of belonging is a central human motivation that has consequences for mental health and well-being, yet surprisingly little research has examined how belonging shapes mental health among young adults. In three data sets from two universities (exploratory study: N = 157; Confirmatory Study 1: N = 121; Confirmatory Study 2: n = 188 in...

Code review is intended to find bugs in early development phases, improving code quality for later integration and testing. However, due to the lack of experience with algorithm design, or software development, individual novice programmers face challenges while reviewing code. In this paper, we utilize collaborative eye tracking to record the gaze...

Background: The coronavirus disease 2019 (COVID-19) pandemic has broad negative impact on physical and mental health of people with chronic neurological disorders such as multiple sclerosis (MS). Objective: We present a machine learning approach leveraging passive sensor data from smartphones and fitness trackers of people with MS to predict the...

Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors c...

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. O...

As new technology inches into every aspect of our lives, there is no place more likely to dramatically change in the future than the workplace. New passive sensing technology is emerging capable of assessing human behavior with the goal of promoting better cognitive and physical capabilities at work. In this article, we survey recent research on th...

Perceived discrimination is common and consequential. Yet, little support is available to ease handling of these experiences. Addressing this gap, we report on a need-finding study to guide us in identifying relevant technologies and their requirements. Specifically, we examined unfolding experiences of perceived discrimination among college studen...

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast...

We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobi...

Background Given possible impairment in psychomotor functioning related to acute cannabis intoxication, we explored whether smartphone-based sensors (e.g., accelerometer) can detect self-reported episodes of acute cannabis intoxication (subjective “high” state) in the natural environment. Methods Young adults (ages 18–25) in Pittsburgh, PA, who re...

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. O...

Objective This study addresses mental health concerns among university students, examining cumulative stress exposure as well as resilience resources. Participants: Participants were 253 first- and second-year undergraduate students (age = 18.76; 49.80% male, 69% students of color) enrolled at a large western US university. Methods: Data were obtai...

Passive mobile sensing for the purpose of human state modeling is a fast-growing area. It has been applied to solve a wide range of behavior-related problems, including physical and mental health monitoring, affective computing, activity recognition, routine modeling, etc. However, in spite of the emerging literature that has investigated a wide ra...

This mixed-method study examined the experiences of college students during the COVID-19 pandemic through surveys, experience sampling data collected over two academic quarters (Spring 2019 n 1 = 253; Spring 2020 n 2 = 147), and semi-structured interviews with 27 undergraduate students. There were no marked changes in mean levels of depressive symp...

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at bot...

Taking an action research approach, we engaged in fieldwork with school-based behavioral health care teams to: observe record keeping practices, design and deploy a prototype system addressing key challenges, and reflect on its use. We describe the challenges of capturing behavioral data using both paper and electronic records. Creating records of...

Assessment of individuals' job performance, personalized health and psychometric measures are domains where data-driven ubiquitous computing will have a profound impact in the near future. Existing work in these domains focus on techniques that use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits to assess wel...

Aim: Acute cannabis intoxication can impair motor skills and cognitive functions. Given possible impairment related to acute cannabis intoxication, we explored whether mobile phone-based sensors (e.g., GPS, text/phone logs) can detect episodes of acute cannabis intoxication (subjective “high” state) as self-reported in natural environments by young...

Background“Homework assignments,” a critical element of Cognitive Behavioral Therapy (CBT) for depression, are a means of CBT skill generalization and maintenance. Yet, homework adherence is often low. We developed CBT MobileWork,© a smartphone application (app) to promote CBT skills practice “as-needed.”Methods We applied a user-centered design in...

This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students’ academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered d...

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and mode...

BACKGROUND Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms. OBJECTIVE The aim...

Background Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms Objective The aim o...

This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students' academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered d...

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select mean...

This paper presents CoRhythMo, the first computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine...

We present a study to detect friendship, its strength, and its change from smartphone location data collectedamong members of a fraternity. We extract a rich set of co-location features and build classifiers that detectfriendships and close friendship at 30% above a random baseline. We design cross-validation schema to testour model performance in...

The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are const...

Assessment of job performance, personalized health and psychometric measures are domains where data-driven and ubiquitous computing exhibits the potential of a profound impact in the future. Existing techniques use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits, to assess well-being and cognitive attributes...

Introduction Sleep is a critical behavior predicting mental health and depressive symptomatology in young adults.The extant scientific literature generally focuses on self-reported sleep measures over relatively short time frames. Here, we examine whether actigraphy-measured sleep variables early in the academic semester predict depressive symptoma...

The impact of COVID-19 on students has been enormous, with an increase in worries about fiscal and physical health, a rapid shift to online learning, and increased isolation. In addition to these changes, students with disabilities/health concerns may face accessibility problems with online learning or communication tools, and their stress may be c...

Background Mobile assessment of the effects of acute marijuana on cognitive functioning in the natural environment would provide an ecologically valid measure of the impacts of marijuana use on daily functioning. Objective This study aimed to examine the association of reported acute subjective marijuana high (rated 0-10) with performance on 3 mob...

BACKGROUND Sedentary behavior (SB) is common after cancer surgery and may negatively affect recovery and quality of life, but postoperative symptoms (e.g., pain) can be a significant barrier to patients achieving recommended physical activity levels. We conducted a single-arm pilot trial evaluating the usability and acceptability of a real-time mob...

Background Sedentary behavior (SB) is common after cancer surgery and may negatively affect recovery and quality of life, but postoperative symptoms such as pain can be a significant barrier to patients achieving recommended physical activity levels. We conducted a single-arm pilot trial evaluating the usability and acceptability of a real-time mob...

Several psychologists posit that performance is not only a function of personality but also of situational contexts, such as day-level activities. Yet in practice, since only personality assessments are used to infer job performance, they provide a limited perspective by ignoring activity. However, multi-modal sensing has the potential to character...

User interfaces are important for streamlining the interactions between humans and computers. However, there are few effective approaches for collecting users’ preferences implicitly and objectively for the purpose of user interface (UI) design optimization. This paper presents an effective approach to interactive genetic algorithm (IGA) optimizati...

A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on c...

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP fo...

Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect reduces the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies uncovered an unexplored side-effect: participants' aversion to typing errors despite autocorrect. We present a com...

The Tesserae project investigates how a suite of sensors can measure workplace performance (e.g., organizational citizenship behavior), psychological traits (e.g., personality, affect), and physical characteristics (e.g., sleep, activity) over one year. We enrolled 757 information workers across the U.S. and measure heart rate, physical activity, s...

The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out of school. Existing work on finding relationships between passively sensed behavior and depression, as well as detecting depression, mainly derives relevant unimodal features from a sing...

The integration of 5G networks and AI benefits to create a more holistic and better connected ecosystem for industries. User profiling has become an important issue for industries to improve company profit. In the 5G era, smartphone applications have become an indispensable part in our everyday lives. Users determine what apps to install based on t...

Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users...

Smartphone apps are becoming ubiquitous in our everyday life. Apps on smartphones sense users' behaviors and activities , providing a lens for understanding users, which is an important point in the community of ubiquitous computing. In UbiComp 2018, we successfully held the first International workshop AppLens 2018: mining and learning from smartp...

The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a “passive sensor” to study human behavior. However, such research is impacted by the lack of homogeneity in...

The number and popularity of smartphone applications is rising dramatically. Users install and use applications depending on their needs and interests. Applications on smartphones convey lots of personal information, providing us a new lens to well profile users. In this paper, we first describe application information for user profiling. Second, w...

Assessing performance in the workplace typically relies on subjective evaluations, such as, peer ratings, supervisor ratings and self assessments, which are manual, burdensome and potentially biased. We use objective mobile sensing data from phones, wearables and beacons to study workplace performance and offer new insights into behavioral patterns...